Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method of measuring breast density, comprising: (a) generating a three-dimensional volume data set of at least a portion of a breast; (b) segmenting the three-dimensional volume data set into volume elements associated with glandular tissue and non-glandular tissue; (c) removing volume elements associated with skin; (d) calculating a volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; and (e) outputting a breast density measurement being a function of the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue.
Breast density measurement is a critical factor in breast cancer screening and diagnosis, as dense breast tissue can obscure tumors in imaging and increase cancer risk. Traditional methods rely on two-dimensional mammography, which provides limited accuracy in assessing volumetric density. This invention addresses the need for precise, three-dimensional breast density quantification. The method involves generating a three-dimensional volume data set of a breast or portion thereof, typically using imaging modalities such as magnetic resonance imaging (MRI) or computed tomography (CT). The data set is then segmented into volume elements (voxels) representing glandular tissue and non-glandular tissue, distinguishing between dense and fatty tissue structures. Skin-related voxels are excluded to focus on internal breast composition. The volumes of glandular and non-glandular tissue are calculated separately, and a breast density measurement is derived as a function of these volumes. This measurement can be expressed as a ratio, percentage, or other quantitative metric, providing a more accurate assessment of breast density compared to two-dimensional methods. The approach enhances diagnostic accuracy and risk stratification in breast cancer screening.
2. The method as recited in claim 1 , wherein segmenting the three-dimensional volume data set generates a histogram of the volume elements; and selects volume elements according to a threshold value distinguishing the glandular tissue and the non-glandular tissue.
This invention relates to medical imaging, specifically the analysis of three-dimensional volume data sets to distinguish glandular tissue from non-glandular tissue. The method involves segmenting the volume data set to generate a histogram of the volume elements (voxels), which represents the distribution of voxel intensities. The segmentation process then selects voxels based on a threshold value that differentiates glandular tissue from non-glandular tissue. This thresholding approach allows for automated classification of tissue types within the volume data, improving diagnostic accuracy and efficiency in medical imaging applications. The method may be applied to various imaging modalities, such as MRI or CT scans, where distinguishing tissue types is critical for diagnosis or treatment planning. By analyzing the histogram of voxel intensities, the system can identify regions corresponding to glandular structures, which often exhibit distinct intensity characteristics compared to surrounding non-glandular tissues. This technique enhances the ability to detect abnormalities or assess tissue composition in medical imaging workflows.
3. The method as recited in claim 1 , wherein the step of removing volume elements associated with skin applies an erosion filter to the three-dimensional volume data set.
This invention relates to medical imaging, specifically to processing three-dimensional volume data sets to remove skin or surface structures. The problem addressed is the presence of skin or surface elements in medical imaging data, which can obscure underlying anatomical features of interest. The solution involves selectively removing these surface elements to enhance visualization of internal structures. The method processes a three-dimensional volume data set, such as from a CT or MRI scan, to isolate and remove volume elements associated with skin or surface layers. This is achieved by applying an erosion filter to the data set. The erosion filter systematically reduces the volume of surface elements, effectively peeling away outer layers while preserving internal anatomical structures. The process may include preprocessing steps to segment or identify skin regions before applying the erosion filter. The method may also adjust filter parameters based on the imaging modality or anatomical region to optimize removal accuracy. The erosion filter operates by iteratively removing boundary voxels (volume elements) from the surface of the data set, gradually reducing the volume of skin or surface structures. The filter may use morphological operations, such as shrinking or thinning, to refine the removal process. The result is a cleaned volume data set with enhanced visibility of internal features, useful for diagnostic or surgical planning applications. The method may also include post-processing steps to smooth or refine the remaining data.
4. The method as recited in claim 1 , further comprising adjusting the breast density measurement according to a patient's age.
This invention relates to medical imaging, specifically to improving the accuracy of breast density measurements in mammography. Breast density is a critical factor in breast cancer risk assessment and screening, but current measurement techniques often produce inconsistent or inaccurate results due to variations in patient demographics, particularly age. The invention addresses this problem by adjusting breast density measurements based on a patient's age to account for physiological changes that occur over time, such as tissue composition shifts or hormonal influences. The method involves capturing a mammographic image of a patient's breast, analyzing the image to determine the initial breast density measurement, and then modifying this measurement using a predefined age-based correction factor. The correction factor is derived from statistical data correlating age with breast density variations, ensuring that the final measurement is more accurate and clinically relevant. This adjustment helps clinicians better assess breast cancer risk and tailor screening protocols accordingly. The invention may also incorporate additional patient-specific factors, such as hormonal status or medical history, to further refine the measurement. By standardizing breast density assessments across different age groups, the method enhances diagnostic reliability and improves patient outcomes.
5. The method as recited in claim 4 , further comprising the step of outputting a standard deviation of the breast density measurement with respect to patients in a particular age group.
This invention relates to breast density measurement and analysis in medical imaging, particularly for assessing breast tissue composition in mammography. The method involves measuring breast density for a patient and comparing it to reference data from a population of patients in a specific age group. The comparison generates a statistical analysis, including a standard deviation, to evaluate how the patient's breast density deviates from the norm for their age group. This helps clinicians assess risk factors for breast cancer, as breast density is a known indicator. The method may also involve normalizing the measurement to account for variations in imaging techniques or patient demographics, ensuring accurate comparisons. By providing a standard deviation, the method quantifies the patient's deviation from the expected range, aiding in personalized risk assessment and treatment planning. The invention improves upon existing techniques by incorporating age-specific statistical analysis, enhancing the precision of breast density evaluations in clinical practice.
6. The method as recited in claim 1 further including the steps of comparing the breast density measurement to data of reference patients to output a risk factor indicating a relative risk of breast cancer for an individual patient.
This invention relates to breast cancer risk assessment using breast density measurements. The method involves analyzing breast density data to determine a patient's relative risk of developing breast cancer by comparing their measurements against a reference dataset of known patient outcomes. The reference data includes breast density measurements from patients with varying cancer histories, allowing for statistical risk stratification. By comparing an individual's breast density to this reference population, the system calculates a risk factor that quantifies their likelihood of developing breast cancer relative to others. This approach leverages the established correlation between breast density and cancer risk, providing a data-driven risk assessment tool for early detection and personalized screening recommendations. The method may also incorporate additional patient-specific factors to refine risk predictions, enhancing clinical decision-making. The system automates risk calculation, reducing subjectivity and improving consistency in breast cancer risk evaluation. This technique supports early intervention strategies by identifying high-risk patients who may benefit from more frequent or advanced screening protocols. The reference data can be continuously updated to improve accuracy as new patient outcomes are recorded. This method addresses the need for objective, evidence-based risk assessment in breast cancer prevention and management.
7. The method as recited in claim 6 , wherein the comparing entails comparing the breast density measurement with a mean breast density value and a standard deviation of the mean breast density value of breast density measurements obtained from a plurality of individual reference patients grouped according to age.
This invention relates to breast density analysis in medical imaging, specifically improving the accuracy of breast cancer risk assessment by comparing an individual's breast density measurement against age-specific reference data. The method involves obtaining a breast density measurement from a patient and comparing it to a mean breast density value and its standard deviation derived from a reference population of patients grouped by age. This comparison helps determine whether the patient's breast density deviates significantly from the expected norm for their age group, which can be used to assess cancer risk more precisely. The reference data is generated by collecting breast density measurements from multiple patients, calculating the mean and standard deviation for each age group, and storing these values for future comparisons. The method ensures that breast density assessments are contextually relevant by accounting for age-related variations in breast tissue composition, improving diagnostic accuracy and personalized risk stratification. This approach enhances early detection and treatment planning by providing a more nuanced understanding of an individual's breast density relative to their peer group.
8. An apparatus for measuring breast density comprising: a table supporting a patient in a prone position with a breast; an x-ray computed tomography (CT) scanner comprising an x-ray source and detector positioned to acquire a CT projection set about a horizontal axis around the breast; and a computer receiving data from the x-ray computed tomography (CT) scanner programmed to: generate a three-dimensional volume data set of at least a portion of the breast; segment the three-dimensional volume data set into volume elements associated with glandular tissue and non-glandular tissue; remove volume elements associated with skin; calculate a volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; and output a breast density measurement being a function of the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue.
Breast density measurement is a critical factor in breast cancer risk assessment and early detection. Traditional methods, such as mammography, provide limited 2D information, making accurate density quantification challenging. This invention addresses the need for precise, volumetric breast density measurement using computed tomography (CT) imaging. The apparatus includes a table designed to support a patient in a prone position, allowing a breast to hang freely for imaging. An x-ray CT scanner, positioned around a horizontal axis, captures a set of projections to generate a 3D volume of the breast. A computer processes the CT data to segment the volume into glandular and non-glandular tissue regions, excluding skin. The system calculates the volume of each tissue type and derives a breast density measurement based on their relative proportions. This approach provides a more accurate and comprehensive assessment of breast density compared to conventional methods, improving diagnostic reliability and risk stratification. The automated segmentation and volumetric analysis enhance efficiency and reduce human error, making it suitable for clinical and research applications.
9. The apparatus of claim 8 , wherein segmenting the three-dimensional volume data set generates a histogram of the volume elements; and selects volume elements according to a threshold value distinguishing the glandular tissue and the non-glandular tissue.
This invention relates to medical imaging, specifically to analyzing three-dimensional volume data sets to distinguish glandular tissue from non-glandular tissue. The problem addressed is the difficulty in accurately segmenting and identifying tissue types in volumetric medical images, which is critical for diagnostic and treatment planning. The apparatus processes a three-dimensional volume data set, such as from a CT or MRI scan, to segment the data into distinct regions. During segmentation, a histogram of the volume elements (voxels) is generated, which represents the distribution of intensity values or other characteristics within the volume. The apparatus then applies a threshold value to this histogram to classify the voxels into glandular and non-glandular tissue categories. The threshold is selected to effectively separate the two tissue types based on their distinct properties in the imaging data. This method improves upon prior techniques by providing a more precise and automated way to differentiate tissue types, reducing manual intervention and increasing diagnostic accuracy. The histogram-based approach allows for adaptive thresholding, ensuring robustness across different imaging conditions and patient variations. The invention is particularly useful in applications such as cancer detection, where distinguishing glandular tissue from surrounding structures is essential.
10. The apparatus of claim 8 , wherein the computer is further programmed to: apply an erosion filter to the three-dimensional volume data set to remove the volume elements associated with skin.
This invention relates to medical imaging and image processing, specifically for analyzing three-dimensional volume data sets to isolate internal anatomical structures. The problem addressed is the difficulty in accurately segmenting internal structures from medical imaging data, such as CT or MRI scans, due to the presence of skin and other surface tissues that obscure the underlying anatomy. The invention provides a solution by applying an erosion filter to the three-dimensional volume data set to remove volume elements associated with skin, thereby isolating the internal structures for further analysis. The apparatus includes a computer programmed to process the three-dimensional volume data set, which may be obtained from medical imaging modalities. The erosion filter is applied to systematically remove outer layers of the volume data, effectively peeling away the skin and surface tissues. This process enhances the visibility and accessibility of internal anatomical features, such as organs, bones, or other structures of interest. The filtered data can then be used for diagnostic purposes, surgical planning, or other medical applications where precise segmentation of internal structures is required. The erosion filter may be adjusted based on the resolution and characteristics of the imaging data to ensure accurate removal of skin while preserving the integrity of the underlying structures. This method improves the efficiency and accuracy of medical image analysis by automating the segmentation process and reducing the need for manual intervention.
11. The apparatus of claim 8 , wherein the computer is further programmed to: adjust the breast density measurement according to a patient's age.
The invention relates to medical imaging systems, specifically to apparatuses for measuring breast density in mammography. Breast density is a critical factor in breast cancer risk assessment, but current measurement techniques often lack precision due to variations in patient demographics, particularly age-related changes in breast tissue composition. The apparatus includes a computer programmed to analyze mammographic images and calculate breast density. To improve accuracy, the computer adjusts the breast density measurement based on the patient's age, accounting for physiological changes such as tissue atrophy or hormonal influences that affect density readings. This adjustment ensures more reliable risk stratification and personalized medical recommendations. The system may also incorporate additional image processing techniques, such as segmentation or texture analysis, to refine density calculations. By integrating age-specific corrections, the apparatus provides a more precise and clinically relevant assessment of breast density, addressing limitations in existing methods that treat all patients uniformly. The invention enhances diagnostic accuracy and supports better-informed clinical decisions.
12. The apparatus of claim 11 , wherein the computer is further programmed to: output a standard deviation of the breast density measurement with respect to patients in a particular age group.
This invention relates to medical imaging and breast density analysis, addressing the need for accurate and age-specific breast density measurements in mammography. The apparatus includes a computer programmed to analyze mammographic images to determine breast density, which is a critical factor in breast cancer risk assessment. The system calculates a standard deviation of the breast density measurement for patients within a specific age group, providing a statistical measure of variability in breast density among individuals of similar age. This allows for more personalized risk stratification and improved diagnostic accuracy. The apparatus may also include a display for visualizing the density measurements and statistical data, aiding radiologists in clinical decision-making. The invention enhances the precision of breast cancer screening by incorporating age-specific density variability into risk assessment models.
13. The apparatus of claim 8 , wherein the computer is further programmed to: compare the breast density measurement to data of reference patients, and output a risk factor indicating a relative risk of breast cancer for an individual patient based on the comparing.
This invention relates to medical imaging and breast cancer risk assessment. The apparatus includes a computer programmed to analyze breast density measurements from medical imaging, such as mammograms, to assess a patient's risk of developing breast cancer. The system compares the patient's breast density data against reference data from a population of patients with known breast cancer outcomes. By analyzing this comparison, the computer generates a risk factor that quantifies the relative likelihood of breast cancer for the individual patient. Higher breast density is associated with an increased risk of breast cancer, and this apparatus provides a standardized, data-driven method to evaluate and communicate that risk. The reference data may include historical patient records, clinical trial data, or other aggregated medical data to establish baseline risk profiles. The output risk factor can be used by healthcare providers to inform screening recommendations, treatment plans, or patient counseling. This approach aims to improve early detection and personalized care by leveraging objective, quantitative analysis of breast density as a known risk factor for breast cancer.
14. The apparatus of claim 13 , wherein the comparing entails comparing the breast density measurement with a mean breast density value and a standard deviation of the mean breast density value of breast density measurements obtained from a plurality of individual reference patients grouped according to age.
This invention relates to medical imaging systems for breast density analysis, specifically addressing the challenge of accurately assessing breast density in mammography to improve early detection of breast cancer. The apparatus includes a processing unit that analyzes mammographic images to measure breast density, which is a critical factor in cancer risk assessment. The system compares an individual patient's breast density measurement against a reference dataset of breast density values from multiple patients, categorized by age. The comparison involves evaluating the patient's measurement against both the mean breast density value and the standard deviation of the mean for the relevant age group. This statistical analysis helps determine whether the patient's breast density deviates significantly from the norm, providing clinicians with more precise risk stratification. The reference dataset is dynamically updated as new patient data is collected, ensuring the comparison remains relevant and accurate over time. This approach enhances diagnostic accuracy by accounting for age-related variations in breast density, reducing false positives and improving personalized risk assessment.
15. The apparatus of claim 8 further comprising: a positron emission tomography (PET) detector positioned to acquire PET data of the breast and to transmit the data to the computer.
This invention relates to a medical imaging system for breast imaging, specifically addressing the need for improved detection and localization of abnormalities in breast tissue. The system combines multiple imaging modalities to enhance diagnostic accuracy. A breast support structure positions the breast for imaging, while a compression mechanism applies controlled pressure to immobilize the breast and reduce motion artifacts. An imaging device, such as a gamma camera or other radiation detector, captures functional imaging data, such as molecular or metabolic activity, by detecting radiation emitted from a radiopharmaceutical administered to the patient. A positron emission tomography (PET) detector is also included to acquire PET data of the breast, providing additional functional imaging information. The PET detector transmits this data to a computer, which processes and integrates it with other imaging data to generate a comprehensive diagnostic image. The system may also include a positioning mechanism to adjust the imaging device relative to the breast, ensuring optimal data acquisition. The combined imaging data improves the detection and characterization of breast lesions, aiding in early diagnosis and treatment planning.
16. The apparatus of claim 15 , wherein the three-dimensional volume data set of at least the portion of the breast is generated based on data produced by the x-ray computed tomography (CT) scanner and the PET data acquired by the PET detector.
This invention relates to medical imaging systems that combine X-ray computed tomography (CT) and positron emission tomography (PET) to generate three-dimensional volume data sets of breast tissue. The system addresses the challenge of obtaining high-resolution anatomical and functional imaging of the breast in a single scan, improving diagnostic accuracy for conditions like breast cancer. The apparatus includes a CT scanner and a PET detector configured to simultaneously acquire X-ray and PET data from at least a portion of a breast. The CT scanner generates X-ray projections, while the PET detector captures positron emission events. A processing unit reconstructs the X-ray data into a CT image and the PET data into a PET image. The system then combines these images to produce a three-dimensional volume data set that integrates anatomical and metabolic information. The combined data set enhances visualization by correlating structural details from CT with functional data from PET, enabling more precise localization of abnormalities. The apparatus may also include a support structure to position the breast for imaging and a calibration system to align the CT and PET data. The invention improves diagnostic workflows by reducing the need for separate imaging sessions, minimizing patient discomfort and radiation exposure.
17. A method of measuring breast density, comprising: (a) generating a three-dimensional volume data set of at least a portion of a breast; (b) segmenting the three-dimensional volume data set into volume elements associated with glandular tissue and non-glandular tissue; (c) calculating a volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; (d) outputting a breast density measurement being a function of the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; and (e) adjusting the breast density measurement according to a patient's age.
Breast density measurement is a critical factor in breast cancer risk assessment and early detection. Traditional methods often rely on two-dimensional mammography, which can be limited in accuracy. This invention describes a method for measuring breast density using three-dimensional imaging data. The process begins by generating a three-dimensional volume data set of at least a portion of a breast, which may be obtained through techniques such as magnetic resonance imaging (MRI), computed tomography (CT), or other volumetric imaging modalities. The data set is then segmented into volume elements, distinguishing between glandular tissue and non-glandular tissue. This segmentation allows for precise calculation of the volumes associated with each tissue type. The breast density measurement is derived as a function of these volumes, providing a quantitative assessment of the proportion of glandular tissue relative to the total breast volume. Additionally, the method adjusts the breast density measurement based on the patient's age, accounting for physiological changes in breast tissue composition over time. This adjustment enhances the accuracy of the measurement, particularly in longitudinal studies or when comparing results across different age groups. The method improves upon existing techniques by leveraging three-dimensional data for more precise and personalized breast density assessments, which can aid in better risk stratification and clinical decision-making.
18. The method as recited in claim 17 , wherein segmenting the three-dimensional volume data set generates a histogram of the volume elements; and selects volume elements according to a threshold value distinguishing the glandular tissue and the non-glandular tissue.
This invention relates to medical imaging, specifically to methods for analyzing three-dimensional volume data sets to distinguish glandular tissue from non-glandular tissue. The problem addressed is the difficulty in accurately segmenting and identifying tissue types in medical imaging data, which is critical for diagnostic and treatment planning purposes. The method involves segmenting a three-dimensional volume data set, such as from a CT scan or MRI, to generate a histogram of the volume elements (voxels). The histogram represents the distribution of voxel intensities or other relevant features. The method then selects voxels based on a threshold value that differentiates glandular tissue from non-glandular tissue. This threshold may be determined statistically, empirically, or through machine learning techniques to ensure accurate segmentation. The segmentation process may involve preprocessing steps like noise reduction or contrast enhancement to improve the quality of the volume data. The thresholding technique can be adaptive, adjusting based on the specific characteristics of the tissue being analyzed. The output is a segmented volume where glandular and non-glandular tissues are clearly distinguished, enabling further analysis or visualization. This approach improves the accuracy and efficiency of tissue classification in medical imaging, aiding in early detection and monitoring of conditions like cancer or other glandular disorders.
19. The method as recited in claim 17 , further including the step of removing volume elements associated with skin before step (c), wherein the step of removing volume elements associated with skin applies an erosion filter to the three-dimensional volume data set.
This invention relates to medical imaging, specifically to processing three-dimensional volume data sets to enhance visualization of internal anatomical structures. The problem addressed is the presence of skin in such data sets, which can obscure underlying tissues and organs, making diagnosis or analysis difficult. The solution involves removing skin-related volume elements from the data set to improve clarity. The method begins by acquiring a three-dimensional volume data set, such as from a CT or MRI scan. Before further processing, an erosion filter is applied to the data to remove volume elements associated with skin. The erosion filter systematically reduces the volume of skin-related data, effectively peeling away the outer layer to reveal internal structures. This step ensures that subsequent analysis or visualization is not hindered by superficial tissue. After skin removal, the method proceeds to analyze the remaining volume data, which may include segmentation, measurement, or other diagnostic tasks. The erosion filter is specifically designed to target skin while preserving the integrity of deeper anatomical features, ensuring accurate and reliable results. This approach is particularly useful in medical imaging where clear visualization of internal structures is critical for diagnosis and treatment planning.
20. The method as recited in claim 17 , further comprising the step of outputting a standard deviation of the breast density measurement with respect to patients in a particular age group.
This invention relates to breast density measurement and analysis in medical imaging, particularly for assessing breast tissue composition in mammography. The method involves analyzing breast density data to determine a patient's breast density measurement and then calculating a standard deviation of this measurement relative to a reference population of patients within a specific age group. This provides a comparative metric to evaluate how a patient's breast density deviates from the norm for their age, aiding in risk assessment and personalized medical decisions. The technique may involve processing mammographic images, applying image analysis algorithms to quantify tissue density, and comparing the results against age-specific statistical data. By incorporating the standard deviation calculation, the method offers a more nuanced understanding of breast density variations, which is critical for early detection of conditions like breast cancer, where density differences can influence diagnosis and treatment strategies. The approach enhances diagnostic accuracy by contextualizing individual measurements within broader demographic trends, improving clinical decision-making.
21. The method as recited in claim 17 , further including the steps of comparing the breast density measurement to data of reference patients to output a risk factor indicating a relative risk of breast cancer for an individual patient.
This invention relates to breast cancer risk assessment using breast density measurements. The method involves analyzing breast density data from medical imaging, such as mammograms, to determine a patient's breast density. The breast density measurement is then compared to a database of reference patient data, which includes breast density measurements and corresponding breast cancer risk information. By comparing the patient's breast density to the reference data, the method calculates a risk factor that indicates the relative likelihood of breast cancer for the individual patient. The reference data may include statistical distributions, historical patient outcomes, or other relevant clinical data to establish correlations between breast density and cancer risk. The method provides a quantitative assessment to aid clinicians in identifying high-risk patients who may require additional screening or preventive measures. The invention improves upon existing risk assessment tools by incorporating objective breast density measurements into the evaluation process, enhancing accuracy and personalization of breast cancer risk predictions.
22. The method as recited in claim 21 , wherein the comparing entails comparing the breast density measurement with a mean breast density value and a standard deviation of the mean breast density value of breast density measurements obtained from a plurality of individual reference patients grouped according to age.
The invention relates to a method for analyzing breast density measurements in medical imaging, specifically addressing the challenge of accurately assessing breast density variations across different patient demographics. Breast density is a critical factor in breast cancer risk assessment and screening effectiveness, but existing methods often lack precise stratification by age, leading to less accurate risk predictions. The method involves obtaining a breast density measurement from a patient and comparing it with reference data derived from a population of individual reference patients. The reference patients are grouped according to age, and the comparison includes evaluating the patient's breast density measurement against a mean breast density value and a standard deviation of the mean for the relevant age group. This approach ensures that the comparison is age-specific, improving the accuracy of breast density assessments and subsequent risk evaluations. The method may also involve generating a risk score or classification based on the comparison, which can guide clinical decisions. By incorporating age-specific reference data, the method enhances the reliability of breast density analysis, particularly in distinguishing normal variations from potentially abnormal findings.
23. An apparatus for measuring breast density comprising: a table supporting a patient in a prone position with a breast; an x-ray computed tomography (CT) scanner comprising an x-ray source and detector positioned to acquire a CT projection set about a horizontal axis around the breast; and a computer receiving data from the x-ray computed tomography (CT) scanner programmed to: generate a three-dimensional volume data set of at least a portion of the breast; segment the three-dimensional volume data set into volume elements associated with glandular tissue and non-glandular tissue; calculate a volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; output a breast density measurement being a function of the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; and adjust the breast density measurement according to a patient's age.
Breast density measurement is a critical factor in early cancer detection, but traditional methods like mammography provide limited volumetric data. This apparatus addresses the need for accurate, three-dimensional breast density assessment. The system includes a table designed to support a patient in a prone position with a breast positioned for imaging. An x-ray computed tomography (CT) scanner, equipped with an x-ray source and detector, acquires a CT projection set around a horizontal axis to capture detailed breast images. A computer processes the data to generate a three-dimensional volume dataset of the breast. The system segments this dataset into volume elements representing glandular and non-glandular tissue. It then calculates the volume of each tissue type within the imaged breast portion. The breast density measurement is derived from these volumes and adjusted based on the patient's age to account for physiological changes. This approach provides a more precise and personalized assessment of breast density compared to conventional methods, improving diagnostic accuracy and risk stratification.
24. The apparatus of claim 23 , wherein segmenting the three-dimensional volume data set generates a histogram of the volume elements; and selects volume elements according to a threshold value distinguishing the glandular tissue and the non-glandular tissue.
This invention relates to medical imaging analysis, specifically a method for distinguishing glandular tissue from non-glandular tissue in three-dimensional (3D) volume data sets, such as those obtained from medical imaging techniques like MRI or CT scans. The problem addressed is the accurate segmentation and classification of tissue types within volumetric medical data, which is crucial for diagnostic and treatment planning purposes. The apparatus processes a 3D volume data set by segmenting it into individual volume elements (voxels). During segmentation, a histogram of the voxel values is generated, which represents the distribution of intensity or other relevant characteristics within the volume. The apparatus then applies a threshold value to this histogram to differentiate between glandular and non-glandular tissue. The threshold is selected based on the distinct properties of these tissue types, allowing for automated or semi-automated classification. This segmentation and thresholding process enables precise identification and isolation of glandular structures, which is essential for applications such as cancer detection, tissue characterization, or treatment monitoring. The method improves upon existing techniques by providing a more reliable and efficient way to analyze complex 3D medical data.
25. The apparatus of claim 23 , wherein the computer is further programmed to: remove volume elements associated with skin before calculating the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; and apply an erosion filter to the three-dimensional volume data set to remove the volume elements associated with skin.
This invention relates to medical imaging and breast tissue analysis, specifically improving the accuracy of volume measurements in breast imaging. The problem addressed is the difficulty in precisely quantifying glandular and non-glandular tissue volumes in breast imaging due to interference from skin tissue, which can distort measurements. The solution involves a computational method that processes three-dimensional volume data sets of breast tissue to isolate and exclude skin-related volume elements before calculating tissue volumes. The apparatus includes a computer programmed to analyze breast imaging data. Before volume calculations, the system removes volume elements associated with skin to prevent their inclusion in tissue measurements. This is achieved by applying an erosion filter to the three-dimensional volume data set, which systematically removes skin-related data points. The erosion filter refines the data by progressively eliminating boundary elements, ensuring that only relevant glandular and non-glandular tissue volumes are considered. This preprocessing step enhances the accuracy of subsequent volume calculations by eliminating noise and artifacts introduced by skin tissue. The method ensures that the final volume measurements for glandular and non-glandular tissues are derived from a cleaned data set, free from skin interference. This approach is particularly useful in medical diagnostics, where precise tissue volume quantification is critical for conditions like breast cancer detection and monitoring. The system's ability to isolate tissue-specific data improves diagnostic reliability and supports more accurate clinical assessments.
26. The apparatus of claim 23 , wherein the computer is further programmed to: output a standard deviation of the breast density measurement with respect to patients in a particular age group.
This invention relates to medical imaging systems, specifically apparatuses for analyzing breast density in mammography. The system addresses the challenge of providing more precise and contextually relevant breast density measurements by incorporating statistical analysis to enhance diagnostic accuracy. The apparatus includes a computer programmed to process mammographic images and calculate breast density measurements. A key feature is the ability to output a standard deviation of the breast density measurement relative to patients within a specific age group. This statistical comparison helps clinicians assess how an individual patient's breast density deviates from normative values for their demographic, improving diagnostic confidence. The system may also include a display for visualizing the breast density measurement alongside the standard deviation data, allowing for intuitive interpretation. Additionally, the computer may be programmed to compare the patient's breast density against a reference database of age-specific measurements, further refining the analysis. This approach ensures that breast density assessments are not only accurate but also contextually relevant, aiding in early detection and personalized treatment planning.
27. The apparatus of claim 23 , wherein the computer is further programmed to: compare the breast density measurement to data of reference patients, and output a risk factor indicating a relative risk of breast cancer for an individual patient based on the comparing.
This invention relates to medical imaging and breast cancer risk assessment. The apparatus includes a computer system that analyzes breast density measurements from imaging scans, such as mammograms, to evaluate a patient's risk of developing breast cancer. The system compares the patient's breast density data against a database of reference patient data, which includes historical breast density measurements and corresponding cancer risk outcomes. By analyzing these comparisons, the computer generates a risk factor that quantifies the relative likelihood of breast cancer for the individual patient. The risk factor is derived from statistical correlations between breast density and cancer incidence in the reference population. The apparatus may also include imaging hardware, such as a mammography machine, to capture the initial breast density measurements. The system aims to provide clinicians with an objective, data-driven assessment of breast cancer risk to support early detection and personalized screening recommendations. The invention addresses the challenge of accurately predicting breast cancer risk based on breast density, which is a known but complex risk factor. The apparatus enhances traditional imaging by integrating quantitative analysis and comparative risk assessment.
28. The apparatus of claim 27 , wherein the comparing entails comparing the breast density measurement with a mean breast density value and a standard deviation of the mean breast density value of breast density measurements obtained from a plurality of individual reference patients grouped according to age.
This invention relates to medical imaging and breast cancer screening, specifically improving the accuracy of breast density measurements for early detection. The problem addressed is the variability in breast density across different age groups, which can lead to misdiagnosis or missed early-stage cancers. The apparatus includes a system that measures breast density from imaging data and compares it against reference data to assess risk. The apparatus first obtains a breast density measurement from a patient using imaging techniques such as mammography or tomosynthesis. It then compares this measurement against a mean breast density value and its standard deviation, derived from a reference population of patients grouped by age. This comparison helps determine whether the patient's breast density deviates significantly from the expected range for their age group, indicating potential risk. The reference data is pre-categorized by age to account for natural variations in breast tissue density as women age, improving diagnostic accuracy. By analyzing deviations from age-specific norms, the system provides a more personalized risk assessment, reducing false positives and negatives. This method enhances early detection by identifying abnormal density patterns that may correlate with higher cancer risk. The apparatus may also include additional features, such as adjusting the reference data based on other patient-specific factors like hormonal status or genetic predisposition, further refining the assessment. The overall goal is to improve the reliability of breast cancer screening by incorporating age-adjusted density comparisons.
29. The apparatus of claim 23 further comprising: a positron emission tomography (PET) detector positioned to acquire PET data of the breast and to transmit the data to the computer.
This invention relates to medical imaging systems for breast cancer detection, specifically addressing the challenge of combining multiple imaging modalities to improve diagnostic accuracy. The apparatus includes a breast imaging system with a compression mechanism to position and immobilize the breast during imaging. The system further incorporates a positron emission tomography (PET) detector designed to acquire PET data of the breast, which is then transmitted to a computer for processing. The PET detector works in conjunction with other imaging components, such as a gamma-ray detector for molecular breast imaging (MBI) and a digital X-ray detector for mammography, to provide complementary data. The computer processes the acquired data to generate a composite image that enhances tumor detection by leveraging the metabolic information from PET alongside structural and functional details from MBI and mammography. This multimodal approach aims to improve early-stage cancer detection by integrating metabolic activity with anatomical and molecular imaging, reducing false positives and increasing diagnostic confidence. The system is designed for clinical use, offering a comprehensive imaging solution for breast cancer screening and diagnosis.
30. The apparatus of claim 29 , wherein the three-dimensional volume data set of at least the portion of the breast is generated based on data produced by the x-ray computed tomography (CT) scanner and the PET data acquired by the PET detector.
This invention relates to medical imaging systems that combine X-ray computed tomography (CT) and positron emission tomography (PET) to generate three-dimensional volume data sets of breast tissue. The system addresses the challenge of obtaining high-resolution anatomical and functional imaging of the breast in a single scan, improving diagnostic accuracy for conditions like breast cancer. The apparatus includes a CT scanner and a PET detector configured to simultaneously acquire X-ray and PET data from at least a portion of a breast. The CT scanner generates X-ray attenuation data, while the PET detector captures positron emission data, which is indicative of metabolic activity. The system processes this combined data to construct a three-dimensional volume representation of the breast tissue, integrating structural and functional information. This hybrid imaging approach enhances lesion detection and characterization by correlating anatomical details from CT with metabolic activity from PET, reducing the need for separate scans and improving diagnostic efficiency. The apparatus may further include a support structure to position the breast during imaging, ensuring consistent data acquisition. The system may also incorporate image reconstruction algorithms to fuse the CT and PET data, optimizing spatial resolution and contrast. This integrated imaging solution provides clinicians with comprehensive diagnostic information in a single examination, potentially reducing patient discomfort and improving workflow in medical imaging facilities.
31. An apparatus for measuring breast density comprising: a table supporting a patient in a prone position with a breast; an x-ray computed tomography (CT) scanner comprising an x-ray source and detector positioned to acquire a CT projection set about a horizontal axis around the breast; and a computer receiving data from the x-ray computed tomography (CT) scanner programmed to: generate a three-dimensional volume data set of at least a portion of the breast; segment the three-dimensional volume data set into volume elements associated with glandular tissue and non-glandular tissue; calculate a volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; output a breast density measurement being a function of the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; compare the breast density measurement to data of reference patients; and output a risk factor indicating a relative risk of breast cancer for an individual patient based on the comparing, wherein the comparing entails comparing the breast density measurement with a mean breast density value and a standard deviation of the mean breast density value of breast density measurements obtained from a plurality of individual reference patients grouped according to age.
This apparatus measures breast density using x-ray computed tomography (CT) to assess breast cancer risk. The system includes a table to support a patient in a prone position with a breast positioned for imaging. An x-ray CT scanner with a source and detector acquires projection data around a horizontal axis to generate a three-dimensional volume of the breast. A computer processes this data to segment the volume into glandular and non-glandular tissue regions, calculating their respective volumes. The system then derives a breast density measurement based on these volumes and compares it to reference data from other patients. The comparison involves evaluating the patient's density against the mean and standard deviation of a reference group matched by age. The system outputs a risk factor indicating the patient's relative breast cancer risk based on this comparison. The method provides a quantitative assessment of breast density and associated cancer risk, improving early detection and personalized risk stratification.
32. The apparatus of claim 31 , wherein segmenting the three-dimensional volume data set generates a histogram of the volume elements; and selects volume elements according to a threshold value distinguishing the glandular tissue and the non-glandular tissue.
This invention relates to medical imaging and tissue analysis, specifically for distinguishing glandular tissue from non-glandular tissue in three-dimensional volume data sets. The technology addresses the challenge of accurately segmenting and analyzing tissue structures in medical imaging, which is critical for diagnostic and treatment planning in fields such as pathology and radiology. The apparatus processes a three-dimensional volume data set, which may be obtained from imaging modalities like MRI, CT, or ultrasound. The system segments the volume data into individual volume elements (voxels) and generates a histogram representing the distribution of these voxels. The histogram is analyzed to identify a threshold value that differentiates glandular tissue from non-glandular tissue based on their distinct characteristics in the imaging data. Volume elements are then selected or classified according to this threshold, enabling precise segmentation of the tissue types. This approach improves upon prior methods by providing an automated and objective way to distinguish tissue types, reducing human error and variability in analysis. The threshold-based selection ensures consistency and accuracy in identifying glandular structures, which is particularly useful in applications such as cancer detection, where accurate tissue differentiation is essential. The system may also include preprocessing steps to enhance image quality and post-processing to refine the segmentation results.
33. The apparatus of claim 31 , wherein the computer is further programmed to: remove volume elements associated with skin before calculating the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; and apply an erosion filter to the three-dimensional volume data set to remove the volume elements associated with skin.
This invention relates to medical imaging and breast tissue analysis, specifically improving the accuracy of volume measurements in breast imaging. The problem addressed is the inclusion of skin volume in breast tissue measurements, which can lead to inaccuracies in assessing glandular and non-glandular tissue volumes. The apparatus includes a computer programmed to process three-dimensional volume data sets of breast tissue. Before calculating the volumes of glandular and non-glandular tissue, the computer removes volume elements associated with skin. This is achieved by applying an erosion filter to the three-dimensional volume data set, which effectively eliminates the skin-related volume elements. The erosion filter modifies the data set by reducing the volume of boundary regions, ensuring that only the relevant breast tissue is analyzed. This preprocessing step enhances the precision of subsequent volume calculations, providing more accurate measurements for medical diagnosis and treatment planning. The invention is particularly useful in breast cancer screening and monitoring, where accurate tissue volume assessment is critical.
34. The apparatus of claim 31 , wherein the computer is further programmed to: adjust the breast density measurement according to a patient's age; and output a standard deviation of the breast density measurement with respect to patients in a particular age group.
This invention relates to medical imaging systems, specifically apparatuses for analyzing breast density in mammography. The technology addresses the challenge of accurately assessing breast density, which is a critical factor in breast cancer risk assessment and early detection. Breast density varies with age, and existing methods may not account for age-related differences, leading to less precise risk evaluations. The apparatus includes a computer programmed to measure breast density from mammographic images. It adjusts the breast density measurement based on the patient's age to improve accuracy. Additionally, the computer calculates and outputs a standard deviation of the breast density measurement relative to patients in the same age group. This provides a comparative metric, allowing clinicians to assess how the patient's density deviates from the norm for their age, enhancing diagnostic precision. The system may also include a display for visualizing the adjusted breast density and the standard deviation, aiding in clinical decision-making. By incorporating age-specific adjustments and statistical comparisons, the apparatus offers a more refined approach to breast density analysis, potentially improving early detection and personalized risk assessment.
35. The apparatus of claim 31 further comprising: a positron emission tomography (PET) detector positioned to acquire PET data of the breast and to transmit the data to the computer.
This invention relates to a medical imaging system for breast imaging, specifically addressing the need for improved diagnostic accuracy in breast cancer detection. The system combines multiple imaging modalities to provide comprehensive data. A breast support structure positions the breast for imaging, while a compression mechanism ensures consistent imaging conditions. The system includes a magnetic resonance imaging (MRI) component that acquires MRI data of the breast and transmits it to a computer for processing. Additionally, a positron emission tomography (PET) detector is positioned to acquire PET data of the breast, which is also transmitted to the computer. The computer processes the MRI and PET data to generate a combined image, enhancing diagnostic capabilities by integrating anatomical and functional information. The system may also include a cooling mechanism to maintain optimal imaging conditions and a user interface for controlling the imaging process. The integration of MRI and PET data allows for more precise localization and characterization of breast lesions, improving early detection and treatment planning.
36. The apparatus of claim 35 , wherein the three-dimensional volume data set of at least the portion of the breast is generated based on data produced by the x-ray computed tomography (CT) scanner and the PET data acquired by the PET detector.
This invention relates to medical imaging systems that combine X-ray computed tomography (CT) and positron emission tomography (PET) to generate three-dimensional volume data sets of breast tissue. The system addresses the challenge of obtaining high-resolution anatomical and functional imaging of the breast in a single scan, improving diagnostic accuracy for conditions like breast cancer. The apparatus includes a CT scanner that captures X-ray attenuation data of the breast and a PET detector that acquires positron emission data. The system processes these data sets to generate a three-dimensional volume representation of at least a portion of the breast. The combined data provides both structural details from CT and metabolic or functional information from PET, enhancing diagnostic capabilities. The invention may include additional features such as a patient support structure to position the breast for imaging, a data processing unit to reconstruct the volume data, and a display for visualizing the combined CT and PET information. The system may also incorporate techniques to align or fuse the CT and PET data sets for accurate co-registration, ensuring precise correlation between anatomical and functional information. This integrated approach improves early detection and characterization of breast abnormalities.
37. A method of measuring breast density, comprising: (a) generating a three-dimensional volume data set of at least a portion of a breast; (b) segmenting the three-dimensional volume data set into volume elements associated with glandular tissue and non-glandular tissue; (c) calculating a volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; (d) outputting a breast density measurement being a function of the volume of the portion of the breast associated with each of the glandular tissue and the non-glandular tissue; (e) comparing the breast density measurement to data of reference patients to output a risk factor indicating a relative risk of breast cancer for an individual patient; and (f) outputting the risk factor indicating the relative risk of breast cancer for the individual patient based on the comparing, wherein the comparing entails comparing the breast density measurement with a mean breast density value and a standard deviation of the mean breast density value of breast density measurements obtained from a plurality of individual reference patients grouped according to age.
Breast density is a critical factor in breast cancer risk assessment, but traditional two-dimensional imaging methods often provide limited accuracy in measuring glandular versus non-glandular tissue. This invention addresses the need for precise, three-dimensional breast density quantification to improve risk stratification. The method involves generating a three-dimensional volume data set of a breast or portion thereof, typically using imaging techniques such as MRI or tomosynthesis. The data set is then segmented into volume elements (voxels) corresponding to glandular and non-glandular tissues. The volumes of these tissue types are calculated separately, and a breast density measurement is derived as a function of these volumes. This measurement is compared to reference data from a population of patients, grouped by age, to determine a risk factor. The comparison involves evaluating the patient's breast density against the mean density and standard deviation of the reference group. The resulting risk factor indicates the patient's relative likelihood of developing breast cancer compared to the reference population. This approach enables more accurate and personalized risk assessment, improving early detection and preventive care strategies.
38. The method as recited in claim 37 , wherein segmenting the three-dimensional volume data set generates a histogram of the volume elements; and selects volume elements according to a threshold value distinguishing the glandular tissue and the non-glandular tissue.
This invention relates to medical imaging, specifically to methods for analyzing three-dimensional volume data sets to distinguish between glandular and non-glandular tissue. The problem addressed is the difficulty in accurately segmenting and classifying tissue types in medical imaging data, which is critical for diagnostic and treatment planning purposes. The method involves processing a three-dimensional volume data set, such as from a CT scan or MRI, to segment the data into distinct regions. During segmentation, a histogram of the volume elements (voxels) is generated, which represents the distribution of intensity values or other relevant features within the volume. The method then applies a threshold value to this histogram to classify the voxels as either glandular or non-glandular tissue. The threshold is selected based on the characteristics of the tissue types, ensuring accurate differentiation. This approach improves upon existing techniques by providing a more precise and automated way to segment and classify tissue, reducing the need for manual intervention and improving diagnostic accuracy. The method is particularly useful in applications such as cancer detection, where distinguishing between glandular and non-glandular tissue is essential for identifying abnormalities.
39. The method as recited in claim 37 , further including the step of removing volume elements associated with skin before step (c), wherein the step of removing volume elements associated with skin applies an erosion filter to the three-dimensional volume data set.
This invention relates to medical imaging, specifically to processing three-dimensional volume data sets to enhance visualization of internal anatomical structures. The problem addressed is the presence of skin or surface data in medical scans, which can obscure underlying structures and complicate analysis. The solution involves removing skin-associated volume elements from the data set before further processing. The method begins with a three-dimensional volume data set, such as from a CT or MRI scan. Before segmenting or analyzing internal structures, the method applies an erosion filter to the data set to remove volume elements associated with skin. The erosion filter systematically reduces or eliminates surface data, effectively peeling away the outer layer to reveal deeper anatomical features. This step ensures that subsequent analysis focuses only on relevant internal structures, improving accuracy and clarity. The erosion filter may be applied uniformly or selectively, depending on the imaging context. The method is particularly useful in medical diagnostics, surgical planning, and anatomical research, where clear visualization of internal structures is critical. By removing skin data early in the processing pipeline, the method streamlines workflows and reduces computational overhead for downstream tasks. The approach is adaptable to various imaging modalities and can be integrated into existing medical imaging software.
40. The method as recited in claim 37 , further comprising adjusting the breast density measurement according to a patient's age.
This invention relates to medical imaging, specifically to improving the accuracy of breast density measurements in mammography. Breast density is a critical factor in breast cancer risk assessment and screening, but current measurements can be affected by patient-specific variables, particularly age. As breast tissue composition changes with age, unadjusted measurements may lead to inaccurate risk assessments or screening recommendations. The method involves capturing a mammographic image of a patient's breast and analyzing the image to determine breast density. The density measurement is then adjusted based on the patient's age to account for age-related changes in breast tissue. This adjustment compensates for variations in glandular and fatty tissue proportions that occur naturally with aging, ensuring more accurate and personalized density assessments. The method may also incorporate additional patient-specific factors, such as hormonal status or medical history, to further refine the measurement. By adjusting breast density measurements for age, the method provides a more reliable indicator of breast cancer risk, improving early detection and personalized screening strategies. This approach enhances the clinical utility of mammographic imaging, particularly in populations where age-related breast tissue changes are significant.
41. The method as recited in claim 40 , further comprising the step of outputting a standard deviation of the breast density measurement with respect to patients in a particular age group.
This invention relates to breast density measurement and analysis in medical imaging, specifically addressing the need for more precise and contextually relevant breast density assessments. The method involves calculating a breast density measurement for a patient based on imaging data, such as mammograms or other breast imaging modalities. The measurement is then compared to a reference database of breast density measurements from other patients, allowing for a comparative analysis. The method further includes categorizing the patient's breast density into predefined density categories, such as fatty, scattered fibroglandular, heterogeneously dense, or extremely dense, based on established medical standards. Additionally, the method outputs a standard deviation of the breast density measurement relative to patients within a specific age group, providing a statistical context for the patient's measurement. This helps clinicians assess how the patient's breast density compares to others of similar age, improving diagnostic accuracy and personalized treatment planning. The method may also involve adjusting the reference database or measurement parameters based on demographic or clinical factors to enhance relevance. The overall goal is to provide a more nuanced and data-driven approach to breast density evaluation, supporting early detection of breast cancer and other conditions.
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February 4, 2020
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